Check-in volume is regarded as the popularity.
Lasso regression model [1]} is employed in this task to predict the popularity. The independent variables are region embeddings, while the dependent variable is the popularity. In this part, we use three metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (\(R^2\) ), to measure the performance of different approaches. The first two metrics are to quantify the errors of prediction, while the last one is to estimate the goodness of fit of the models. We measure the performance using these three metrics by K-Fold cross-validation, where K is set as 5. The value of the L1 normalization weight of Lasso regression is chosen by grid searching.
